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b3b33urob
tutorials
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Labs
Datum
Č.T.
S/L
Náplň
Učitel
Materiály
25-26.09.2023
1
L
Intro:
Introduction to the course, Python, NumPy, GitHub, Jupyter Notebook and the environment.
PV
Python & Numpy
Notebook
Notebook (Zip)
02-03.10.2023
2
S
1D regression and 2D classification:
Revision of the regression and classification theory, analytic gradient computation, gradient in computational graph and loss minimization.
KZ
template_for_students.zip
labs_02_regression.pdf
09-10.10.2023
3
L
Autograd:
Computational graphs, backpropagation and the automatic gradient computation.
JV
HW1: Autograd
engine_lab
engine_ref
lab03.pdf
16-17.10.2023
4
S
Classification I - Intro:
Revision of the classification theory, KNN and the linear classifier.
AK
23-24.10.2023
5
L
Classification II - NN:
Classification using neural networks.
AK
HW2: Classification
30-31.10.2023
6
S
CNN I - Intro:
Introduction to the convolution and to the PyTorch library.
JV
lab06.zip
lab06_ref.zip
06-07.11.2023
7
L
Optimization:
Convergence rate, oscillations, diminishing gradients.
KZ
optimizers_student_template.py.zip
13-14.11.2023
8
S
CNN II: Training a classifier:
Training a simple CNN classifier.
JV
lab08.zip
lab08_ref.zip
20-21.11.2023
9
L
Dean's day
27-28.11.2023
10
S
CNN III - Semantic segmentation:
Segmentation of images using CNN.
PV
HW 3 - Segmentation
HW3
04-05.12.2023
11
L
RL I Intro:
Introduction to the reinforcement learning. Policy gradient.
TT
HW4: Reinforcement learning
11-12.12.2023
12
S
RL II - Deep learning:
Deep reinforcement learning.
TT
18-19.12.2023
13
L
CNN IV - Semantic Segmentation:
Presentations, Team projects
PV
08-09.01.2023
14
S
Transformers:
Applications of the transformers.
DC
lab05.zip
courses/b3b33urob/tutorials/start.txt
· Last modified: 2024/01/08 16:01 by
capekda4